1. Field of the Invention
The invention relates to a procedure for extracting information from a phonocardiographic signal obtained from a transducer and subjected to signal processing in order to aid evaluation and diagnosis of heart conditions. The invention furthermore relates to techniques forming part of such extraction and apparatus to perform such feature extraction as well as coding the features to aid the ability to distinguish between related features.
2. Description of Related Art
Signals obtained by means of a transducer are phonocardiographic representations of sounds traditionally listened to by means of a stethoscope. Training in auscultation takes a long time and requires an aptitude for recognising and classifying aural cues, frequently in a noisy environment. 20-30 different conditions may need to be differentiated, and within each, the severity evaluated. Furthermore, there may be combinations among these. These factors contribute to explaining why not all physicians perform equally well when diagnosing heart conditions, and why it may be time-consuming.
The so-called first (S1) and second (S2) heart sound are very important markers in the assessment of a heart sound signal. These sounds are directly related to the functioning of the heart valves, in that S1 is caused by the closure of the atrioventricular valves and contraction of the ventricles and S2 is caused by the closure of the aortic and pulmonary valves.
It has been tried to use further signals derived from ECG signals to determine the points in time during which to expect specific heart sounds, such as U.S. Pat. No. 5,685,317, and as described in Haghighi-Mood, A. et al. “A sub-band energy tracking algorithm for heart sound segmentation”, In: Computers in Cardiology 1995, Vienna, Austria, 10-13 Sep. 1995, pp. 501-504, which latter is model-based (AR). The extra complication of using ECG in addition to phonocardiographic signals is not generally desirable.
A number of patents relate to the extraction of the S1 and S2 signals, such as U.S. Pat. No. 6,048,319, which concerns the measurement of the time interval between the S1 and S2 signals in relation to the heart rate in order to determine the degree of coronary artery disease. The measurement is based on peak detection and autocorrelation and it may be considered a relatively slow process.
In order to determine the occurrence of the first and second heart sounds a wavelet analysis and re-synthesis and various time occurrence manipulations are used in Huiying, L. et al. “A heart sound segmentation algorithm using wavelet decomposition and reconstruction”, ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, 1997. PROCEEDINGS OF THE 19TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE, Chicago, Ill., USA, 30 Oct.-2 Nov. 1997, Vol. 4, pp. 1630-1633. It is described as being a good basis for further analysis of heart sound signals.
In PCT Application Publication WO 02/096293 Ala complex procedure is described, comprising the use of wavelets, calculating the signal's Shannon's energy, calculating the area of each of a number of energy envelopes, and performing cluster analysis. The latter is needed to identify the S1 and S2 signals, but it is a complicated procedure, and the output of the complex procedure is a number of diagnoses, including murmur.
A different category of signals related to various heart conditions is generally known as murmurs. The known procedures of isolating and categorizing murmurs are generally dependent on the simultaneous recording of electrocardiographic data, such as U.S. Pat. Nos. 5,957,866 and 6,050,950 and this complicates the practical use of auscultation techniques considerably.
The above solutions are very complex and rely on techniques that are equivalent to a long averaging time. According to the invention a method has been derived which is more precise and obtains a faster result. This is obtained by a sequence of steps, comprising an optional adaptive noise reduction, detection of S1 and S2, e.g. by means of the feature extraction procedure mentioned above, enhancement of the signal by elimination of the S1 and S2 contributions, performing spectral analysis and feature enhancement in order to obtain the energy content present in areas of a time-frequency representation delimited by frequency band times time interval in the form of energy distributions, classifying the energy distributions according to pre-defined criteria, and comparing the energy distributions to a catalogue of distributions related to known medical conditions and extracting information by comparing the enhanced signal to stored time functions.